In this book, authors Holden Karau and Boris Lublinsky show you how to scale existing Python applications and pipelines, allowing you to stay in the Python ecosystem while avoiding single points of failure and manual scheduling.
With this short but thorough resource, data scientists and Python programmers will learn how the Dask open source library for parallel computing provides APIs that make it easy to parallelize PyData libraries including NumPy, pandas, and scikit-learn.
Authors Holden Karau, Rachel Warren, and Anya Bida walk you through the secrets of the Spark code base, and demonstrate performance optimizations that will help your data pipelines run faster, scale to larger datasets, and avoid costly antipatterns.